{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "02adde30-2633-41f1-a672-af8ff83a1b02",
    "_uuid": "22754d0cc0847be93bf947c7998d7fb65a2817d7"
   },
   "source": [
    "**Objective of the competition:**\n",
    "\n",
    "The competition dataset contains text from works of fiction written by spooky authors of the public domain: \n",
    " 1. Edgar Allan Poe (EAP)\n",
    " 2. HP Lovecraft (HPL)\n",
    " 3. Mary Wollstonecraft Shelley (MWS)\n",
    " \n",
    "The objective  is to accurately identify the author of the sentences in the test set.\n",
    "\n",
    "**Objective of the notebook:**\n",
    "\n",
    "In this notebook, let us try to create different features that will help us in identifying the spooky authors. \n",
    "\n",
    "As a first step, we will do some basic data visualization and cleaning before we delve deep into the feature engineering part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b31f62fb-bde8-410e-972c-3c092f22d497",
    "_uuid": "0fcdf81ce439d2215892af58f839edfc0ca80a91",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "import xgboost as xgb\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn import ensemble, metrics, model_selection, naive_bayes\n",
    "color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "eng_stopwords = set(stopwords.words(\"english\"))\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "add1b71c-e802-408f-8f62-7ea71ed155cf",
    "_uuid": "ae86f9515b3be4956e223db4c2472c2c0c40d9fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in train dataset :  19579\n",
      "Number of rows in test dataset :  8392\n"
     ]
    }
   ],
   "source": [
    "## Read the train and test dataset and check the top few lines ##\n",
    "train_df = pd.read_csv(\"../input/train.csv\")\n",
    "test_df = pd.read_csv(\"../input/test.csv\")\n",
    "print(\"Number of rows in train dataset : \",train_df.shape[0])\n",
    "print(\"Number of rows in test dataset : \",test_df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "35e22f81-77f9-438e-ba50-803aea15ad14",
    "_uuid": "83a9dfc6af30753f82f07ef6162d3b9ba155b06d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id26305</td>\n",
       "      <td>This process, however, afforded me no means of...</td>\n",
       "      <td>EAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id17569</td>\n",
       "      <td>It never once occurred to me that the fumbling...</td>\n",
       "      <td>HPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id11008</td>\n",
       "      <td>In his left hand was a gold snuff box, from wh...</td>\n",
       "      <td>EAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id27763</td>\n",
       "      <td>How lovely is spring As we looked from Windsor...</td>\n",
       "      <td>MWS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id12958</td>\n",
       "      <td>Finding nothing else, not even gold, the Super...</td>\n",
       "      <td>HPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                                               text author\n",
       "0  id26305  This process, however, afforded me no means of...    EAP\n",
       "1  id17569  It never once occurred to me that the fumbling...    HPL\n",
       "2  id11008  In his left hand was a gold snuff box, from wh...    EAP\n",
       "3  id27763  How lovely is spring As we looked from Windsor...    MWS\n",
       "4  id12958  Finding nothing else, not even gold, the Super...    HPL"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5bee9e9a-5fbf-4ee6-9e92-0c77821fe77d",
    "_uuid": "d7682e0812990e3409db6076e7570ce984e466a1"
   },
   "source": [
    "We can check the number of occurrence of each of the author to see if the classes are balanced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "95529569-3380-4794-8248-caf5e8c67f6a",
    "_uuid": "045fe5712b26a72855dad61f05c947ab829a295b"
   },
   "outputs": [
    {
     "data": {
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12cCygeNWttgdbX1iXJIkTdJ6nwRU1SrgScCZdI/d/7x9ngU8qapWruf032i9CG6qqgvX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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c36d5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt_srs = train_df['author'].value_counts()\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "sns.barplot(cnt_srs.index, cnt_srs.values, alpha=0.8)\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Author Name', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bff5de3a-b92f-4192-b142-60d0367e1bb2",
    "_uuid": "7b1ddc8bf782ae87ed5c24fdb6edbff986255d60"
   },
   "source": [
    "This looks good. There is not much class imbalance. Let us print some lines of each of the authors to try and understand their writing style if possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "6c85f84f-d0ee-444e-8b5f-2bf62452624a",
    "_uuid": "cc6b841404940a0f84481c76f5c2328b373416e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author name :  EAP\n",
      "This process, however, afforded me no means of ascertaining the dimensions of my dungeon; as I might make its circuit, and return to the point whence I set out, without being aware of the fact; so perfectly uniform seemed the wall.\n",
      "In his left hand was a gold snuff box, from which, as he capered down the hill, cutting all manner of fantastic steps, he took snuff incessantly with an air of the greatest possible self satisfaction.\n",
      "The astronomer, perhaps, at this point, took refuge in the suggestion of non luminosity; and here analogy was suddenly let fall.\n",
      "The surcingle hung in ribands from my body.\n",
      "I knew that you could not say to yourself 'stereotomy' without being brought to think of atomies, and thus of the theories of Epicurus; and since, when we discussed this subject not very long ago, I mentioned to you how singularly, yet with how little notice, the vague guesses of that noble Greek had met with confirmation in the late nebular cosmogony, I felt that you could not avoid casting your eyes upward to the great nebula in Orion, and I certainly expected that you would do so.\n",
      "\n",
      "\n",
      "Author name :  HPL\n",
      "It never once occurred to me that the fumbling might be a mere mistake.\n",
      "Finding nothing else, not even gold, the Superintendent abandoned his attempts; but a perplexed look occasionally steals over his countenance as he sits thinking at his desk.\n",
      "Herbert West needed fresh bodies because his life work was the reanimation of the dead.\n",
      "The farm like grounds extended back very deeply up the hill, almost to Wheaton Street.\n",
      "His facial aspect, too, was remarkable for its maturity; for though he shared his mother's and grandfather's chinlessness, his firm and precociously shaped nose united with the expression of his large, dark, almost Latin eyes to give him an air of quasi adulthood and well nigh preternatural intelligence.\n",
      "\n",
      "\n",
      "Author name :  MWS\n",
      "How lovely is spring As we looked from Windsor Terrace on the sixteen fertile counties spread beneath, speckled by happy cottages and wealthier towns, all looked as in former years, heart cheering and fair.\n",
      "A youth passed in solitude, my best years spent under your gentle and feminine fosterage, has so refined the groundwork of my character that I cannot overcome an intense distaste to the usual brutality exercised on board ship: I have never believed it to be necessary, and when I heard of a mariner equally noted for his kindliness of heart and the respect and obedience paid to him by his crew, I felt myself peculiarly fortunate in being able to secure his services.\n",
      "I confess that neither the structure of languages, nor the code of governments, nor the politics of various states possessed attractions for me.\n",
      "He shall find that I can feel my injuries; he shall learn to dread my revenge\" A few days after he arrived.\n",
      "He had escaped me, and I must commence a destructive and almost endless journey across the mountainous ices of the ocean, amidst cold that few of the inhabitants could long endure and which I, the native of a genial and sunny climate, could not hope to survive.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "grouped_df = train_df.groupby('author')\n",
    "for name, group in grouped_df:\n",
    "    print(\"Author name : \", name)\n",
    "    cnt = 0\n",
    "    for ind, row in group.iterrows():\n",
    "        print(row[\"text\"])\n",
    "        cnt += 1\n",
    "        if cnt == 5:\n",
    "            break\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "84398b75-325f-4259-90ea-bb6293cc5235",
    "_uuid": "d5739c4a5967d2bcb9326d39529f84791c09dfb8"
   },
   "source": [
    "Only thing I can see is that there are quite a few special characters present in the text data. So count of these special characters might be a good feature. Probably we can create them later.\n",
    "\n",
    "Apart from that, I do not have much clue.. In case if you find any interesting styles (features which we can create), please add them in the comments. \n",
    "\n",
    "**Feature Engineering:**\n",
    "\n",
    "Now let us come try to do some feature engineering. This consists of two main parts.\n",
    "\n",
    " 1. Meta features - features that are extracted from the text like number of words, number of stop words, number of punctuations etc\n",
    " 2. Text based features - features directly based on the text / words like frequency, svd, word2vec etc.\n",
    "\n",
    "**Meta Features:**\n",
    "\n",
    "We will start with creating meta featues and see how good are they at predicting the spooky authors. The feature list is as follows:\n",
    "1. Number of words in the text\n",
    "2. Number of unique words in the text\n",
    "3. Number of characters in the text\n",
    "4. Number of stopwords \n",
    "5. Number of punctuations\n",
    "6. Number of upper case words\n",
    "7. Number of title case words\n",
    "8. Average length of the words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "5758c1ff-ca4d-4c66-8c85-ac1725bd631b",
    "_uuid": "ec015064f318cfd7d0a405bd28cd002f72724bbe",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Number of words in the text ##\n",
    "train_df[\"num_words\"] = train_df[\"text\"].apply(lambda x: len(str(x).split()))\n",
    "test_df[\"num_words\"] = test_df[\"text\"].apply(lambda x: len(str(x).split()))\n",
    "\n",
    "## Number of unique words in the text ##\n",
    "train_df[\"num_unique_words\"] = train_df[\"text\"].apply(lambda x: len(set(str(x).split())))\n",
    "test_df[\"num_unique_words\"] = test_df[\"text\"].apply(lambda x: len(set(str(x).split())))\n",
    "\n",
    "## Number of characters in the text ##\n",
    "train_df[\"num_chars\"] = train_df[\"text\"].apply(lambda x: len(str(x)))\n",
    "test_df[\"num_chars\"] = test_df[\"text\"].apply(lambda x: len(str(x)))\n",
    "\n",
    "## Number of stopwords in the text ##\n",
    "train_df[\"num_stopwords\"] = train_df[\"text\"].apply(lambda x: len([w for w in str(x).lower().split() if w in eng_stopwords]))\n",
    "test_df[\"num_stopwords\"] = test_df[\"text\"].apply(lambda x: len([w for w in str(x).lower().split() if w in eng_stopwords]))\n",
    "\n",
    "## Number of punctuations in the text ##\n",
    "train_df[\"num_punctuations\"] =train_df['text'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )\n",
    "test_df[\"num_punctuations\"] =test_df['text'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )\n",
    "\n",
    "## Number of title case words in the text ##\n",
    "train_df[\"num_words_upper\"] = train_df[\"text\"].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
    "test_df[\"num_words_upper\"] = test_df[\"text\"].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
    "\n",
    "## Number of title case words in the text ##\n",
    "train_df[\"num_words_title\"] = train_df[\"text\"].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
    "test_df[\"num_words_title\"] = test_df[\"text\"].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
    "\n",
    "## Average length of the words in the text ##\n",
    "train_df[\"mean_word_len\"] = train_df[\"text\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))\n",
    "test_df[\"mean_word_len\"] = test_df[\"text\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "164b0d54-3d9e-4b58-ae7c-20c447efdc69",
    "_uuid": "d79f96166a7fe609fd8431b341b2d5ad189ca07a"
   },
   "source": [
    "Let us now plot some of our new variables to see of they will be helpful in predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "fe9a91d9-3df7-4e1b-9e69-f701cc714a15",
    "_uuid": "51962b52791977deefab8b7991d59a51211c8a5e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9ZoS6Ih4s8qJALRKN4eFhAFzVAs9rla/dT0qHArVEYikj1Gr5EAlfbiaB2YEa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2AAAg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\nqEVEREREIvC/sWYehnQ3a4YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c3371748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['num_words'].loc[train_df['num_words']>80] = 80 #truncation for better visuals\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.violinplot(x='author', y='num_words', data=train_df)\n",
    "plt.xlabel('Author Name', fontsize=12)\n",
    "plt.ylabel('Number of words in text', fontsize=12)\n",
    "plt.title(\"Number of words by author\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "66833874-08f2-48b8-9561-b54de997071d",
    "_uuid": "abe5dcadd42cd45169e44e3e299c303d5197b8ab",
    "collapsed": true
   },
   "source": [
    "EAP seems slightly lesser number of words than MWS and HPL. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "91514e74-d734-4642-a9dd-470cb8a65733",
    "_uuid": "1c4c0950d33e29d270fbc021aa8c5ff0e7ab205b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J0OzsLC3Riy3Uhftzc3P1CkmkafX39xNJlZhWzfnzV0vzUEItgdm2bRsAkZmR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VAAAf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\ni4iIiIhEQIFaRERERCQC/x/7vf0XQu290QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c33a1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['num_punctuations'].loc[train_df['num_punctuations']>10] = 10 #truncation for better visuals\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.violinplot(x='author', y='num_punctuations', data=train_df)\n",
    "plt.xlabel('Author Name', fontsize=12)\n",
    "plt.ylabel('Number of puntuations in text', fontsize=12)\n",
    "plt.title(\"Number of punctuations by author\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3a622bfc-da8b-4f13-b9d8-a63dd79a5218",
    "_uuid": "240a9ac16cf5d342e6d01cb020f1d8cea670ec84"
   },
   "source": [
    "This also seems to be somewhat useful. Now let us focus on creating some text based features. \n",
    "\n",
    "Let us first build a basic model to see how these meta features  are helping. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "d97fc321-32a9-429e-9135-ae52300332ec",
    "_uuid": "c27a85339ef5cf5ac3c87527249f878b615d3996",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Prepare the data for modeling ###\n",
    "author_mapping_dict = {'EAP':0, 'HPL':1, 'MWS':2}\n",
    "train_y = train_df['author'].map(author_mapping_dict)\n",
    "train_id = train_df['id'].values\n",
    "test_id = test_df['id'].values\n",
    "\n",
    "### recompute the trauncated variables again ###\n",
    "train_df[\"num_words\"] = train_df[\"text\"].apply(lambda x: len(str(x).split()))\n",
    "test_df[\"num_words\"] = test_df[\"text\"].apply(lambda x: len(str(x).split()))\n",
    "train_df[\"mean_word_len\"] = train_df[\"text\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))\n",
    "test_df[\"mean_word_len\"] = test_df[\"text\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))\n",
    "\n",
    "cols_to_drop = ['id', 'text']\n",
    "train_X = train_df.drop(cols_to_drop+['author'], axis=1)\n",
    "test_X = test_df.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c4198d3f-5fad-41b1-9bcf-3b2faef2287f",
    "_uuid": "1fddaea1621cba593f86d78851a26e1645f4954e"
   },
   "source": [
    "We can train a simple XGBoost model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "1534f55b-1334-4f1e-a597-d51e4d190fb3",
    "_uuid": "7e736a33d7aefcc49edb0e629e57462f92e86c99",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runXGB(train_X, train_y, test_X, test_y=None, test_X2=None, seed_val=0, child=1, colsample=0.3):\n",
    "    param = {}\n",
    "    param['objective'] = 'multi:softprob'\n",
    "    param['eta'] = 0.1\n",
    "    param['max_depth'] = 3\n",
    "    param['silent'] = 1\n",
    "    param['num_class'] = 3\n",
    "    param['eval_metric'] = \"mlogloss\"\n",
    "    param['min_child_weight'] = child\n",
    "    param['subsample'] = 0.8\n",
    "    param['colsample_bytree'] = colsample\n",
    "    param['seed'] = seed_val\n",
    "    num_rounds = 2000\n",
    "\n",
    "    plst = list(param.items())\n",
    "    xgtrain = xgb.DMatrix(train_X, label=train_y)\n",
    "\n",
    "    if test_y is not None:\n",
    "        xgtest = xgb.DMatrix(test_X, label=test_y)\n",
    "        watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]\n",
    "        model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50, verbose_eval=20)\n",
    "    else:\n",
    "        xgtest = xgb.DMatrix(test_X)\n",
    "        model = xgb.train(plst, xgtrain, num_rounds)\n",
    "\n",
    "    pred_test_y = model.predict(xgtest, ntree_limit = model.best_ntree_limit)\n",
    "    if test_X2 is not None:\n",
    "        xgtest2 = xgb.DMatrix(test_X2)\n",
    "        pred_test_y2 = model.predict(xgtest2, ntree_limit = model.best_ntree_limit)\n",
    "    return pred_test_y, pred_test_y2, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1effdaf3-faca-455d-a7a2-ae4d81b5c209",
    "_uuid": "607c32af563879d1f7e011a438216ab8bea74ae3"
   },
   "source": [
    "For the sake of kernel run time, we can just check the first fold in the k-fold cross validation for the scores. Please remove the 'break' line while running in local."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "97965af9-da5a-4ccc-94c5-bc541ed00d49",
    "_uuid": "441491131b9a7272863714494dd1303022c9d630"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mlogloss:1.09449\ttest-mlogloss:1.09516\n",
      "Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-mlogloss hasn't improved in 50 rounds.\n",
      "[20]\ttrain-mlogloss:1.04432\ttest-mlogloss:1.05531\n",
      "[40]\ttrain-mlogloss:1.02333\ttest-mlogloss:1.03909\n",
      "[60]\ttrain-mlogloss:1.0077\ttest-mlogloss:1.02641\n",
      "[80]\ttrain-mlogloss:0.995189\ttest-mlogloss:1.01622\n",
      "[100]\ttrain-mlogloss:0.987562\ttest-mlogloss:1.01193\n",
      "[120]\ttrain-mlogloss:0.980602\ttest-mlogloss:1.00762\n",
      "[140]\ttrain-mlogloss:0.975115\ttest-mlogloss:1.00435\n",
      "[160]\ttrain-mlogloss:0.970039\ttest-mlogloss:1.00162\n",
      "[180]\ttrain-mlogloss:0.965437\ttest-mlogloss:0.999482\n",
      "[200]\ttrain-mlogloss:0.961197\ttest-mlogloss:0.997566\n",
      "[220]\ttrain-mlogloss:0.957497\ttest-mlogloss:0.996358\n",
      "[240]\ttrain-mlogloss:0.954508\ttest-mlogloss:0.995064\n",
      "[260]\ttrain-mlogloss:0.951444\ttest-mlogloss:0.994385\n",
      "[280]\ttrain-mlogloss:0.947614\ttest-mlogloss:0.99326\n",
      "[300]\ttrain-mlogloss:0.944491\ttest-mlogloss:0.992291\n",
      "[320]\ttrain-mlogloss:0.941174\ttest-mlogloss:0.990842\n",
      "[340]\ttrain-mlogloss:0.938057\ttest-mlogloss:0.989517\n",
      "[360]\ttrain-mlogloss:0.9356\ttest-mlogloss:0.989211\n",
      "[380]\ttrain-mlogloss:0.93295\ttest-mlogloss:0.988505\n",
      "[400]\ttrain-mlogloss:0.930593\ttest-mlogloss:0.988319\n",
      "[420]\ttrain-mlogloss:0.927962\ttest-mlogloss:0.987847\n",
      "[440]\ttrain-mlogloss:0.925591\ttest-mlogloss:0.987498\n",
      "[460]\ttrain-mlogloss:0.923052\ttest-mlogloss:0.987225\n",
      "[480]\ttrain-mlogloss:0.921052\ttest-mlogloss:0.987419\n",
      "[500]\ttrain-mlogloss:0.919184\ttest-mlogloss:0.987374\n",
      "Stopping. Best iteration:\n",
      "[463]\ttrain-mlogloss:0.922716\ttest-mlogloss:0.987117\n",
      "\n",
      "cv scores :  [0.98711690099042315]\n"
     ]
    }
   ],
   "source": [
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2017)\n",
    "cv_scores = []\n",
    "pred_full_test = 0\n",
    "pred_train = np.zeros([train_df.shape[0], 3])\n",
    "for dev_index, val_index in kf.split(train_X):\n",
    "    dev_X, val_X = train_X.loc[dev_index], train_X.loc[val_index]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    pred_val_y, pred_test_y, model = runXGB(dev_X, dev_y, val_X, val_y, test_X, seed_val=0)\n",
    "    pred_full_test = pred_full_test + pred_test_y\n",
    "    pred_train[val_index,:] = pred_val_y\n",
    "    cv_scores.append(metrics.log_loss(val_y, pred_val_y))\n",
    "    break\n",
    "print(\"cv scores : \", cv_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ef5d6aa2-9d59-48e8-87ad-7ab254e0dc8a",
    "_uuid": "6ebaf270a27c8b07455498111bd0a8b2c11b6b16"
   },
   "source": [
    "We are getting a mlogloss of '0.987' using just the meta features. Not a bad score. Now let us see which of these features are important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "2856aa57-cce7-4f18-af56-a77ebd52eef7",
    "_uuid": "506938cebfedaad3e70d531e4cad554abe2a3c55"
   },
   "outputs": [
    {
     "data": {
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XNc+ECRM4//zzGTVqFNdeey2XXHJJB1eq7qzEfcwmKo8g3Qt8aMO7dsqcd1C5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\nSZJUlKFEkiRJUlH/HwlwTpClAzK7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20c3278ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the important variables ###\n",
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "xgb.plot_importance(model, max_num_features=50, height=0.8, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "739ce74e-af56-4fd6-a7b3-fe809f33bc54",
    "_uuid": "25225b8d3a5ed46ec90308478b3788760eb5ea1d"
   },
   "source": [
    "Number of characters, mean word length and number of unique words turn out to be the top 3 variables. Now let us focus on creating some text based features. \n",
    "\n",
    "**Text Based Features :**\n",
    "\n",
    "One of the basic features which we could create is tf-idf values of the words present in the text. So we can start with that one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "dd89dcab-b7a2-4b11-9564-ac8558f938e0",
    "_uuid": "41b4430c7e9699bd7f430c471009082bf7449928",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Fit transform the tfidf vectorizer ###\n",
    "tfidf_vec = TfidfVectorizer(stop_words='english', ngram_range=(1,3))\n",
    "tfidf_vec.fit(train_df['text'].values.tolist() + test_df['text'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['text'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['text'].values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fb46183a-43b6-4785-823a-f017a0821b78",
    "_uuid": "9c51efb2c3ff436baf8811a298b699b430b812bf"
   },
   "source": [
    "Now that we have got the tfidf vector, here is the tricky part. The tfidf output is a sparse matrix and so if we have to use it with other dense features, we have couple of choices. \n",
    "1. We can choose to get the top 'n' features (depending on the system config) from the tfidf vectorizer, convert it into dense format and concat with other features. \n",
    "2. Build a model using just the sparse features and then use the predictions as one of the features along with other dense features.\n",
    "\n",
    "Based on the dataset, one might perform better than the other. Here we can use the second approach since there are some very [good scoring kernels](https://www.kaggle.com/the1owl/python-tell-tale-tutorial) using all the features of tfidf.\n",
    "\n",
    "Also it seems that, [Naive Bayes is performing better](https://www.kaggle.com/thomasnelson/spooky-simple-naive-bayes-scores-0-399) in this dataset. So we could build a naive bayes model using tfidf features as it is faster to train."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "7ce26604-5935-41cd-85ac-33e499a186f1",
    "_uuid": "924a649ddf02867e7bd1e68d8cab33d3c36bb83e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def runMNB(train_X, train_y, test_X, test_y, test_X2):\n",
    "    model = naive_bayes.MultinomialNB()\n",
    "    model.fit(train_X, train_y)\n",
    "    pred_test_y = model.predict_proba(test_X)\n",
    "    pred_test_y2 = model.predict_proba(test_X2)\n",
    "    return pred_test_y, pred_test_y2, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Naive Bayes on Word Tfidf Vectorizer:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "61063815-507a-45f7-8866-5298da11e62d",
    "_uuid": "c0ed8394d5fbcfb833efa2836509583a2ec79de1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cv score :  0.842216198361\n"
     ]
    }
   ],
   "source": [
    "cv_scores = []\n",
    "pred_full_test = 0\n",
    "pred_train = np.zeros([train_df.shape[0], 3])\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2017)\n",
    "for dev_index, val_index in kf.split(train_X):\n",
    "    dev_X, val_X = train_tfidf[dev_index], train_tfidf[val_index]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    pred_val_y, pred_test_y, model = runMNB(dev_X, dev_y, val_X, val_y, test_tfidf)\n",
    "    pred_full_test = pred_full_test + pred_test_y\n",
    "    pred_train[val_index,:] = pred_val_y\n",
    "    cv_scores.append(metrics.log_loss(val_y, pred_val_y))\n",
    "print(\"Mean cv score : \", np.mean(cv_scores))\n",
    "pred_full_test = pred_full_test / 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "404695a0-c33b-4cba-9dd1-bf2a5e8c12fc",
    "_uuid": "e16c6d867d6bf1bb4e2661cc28841c672091fe0a"
   },
   "source": [
    "We are getting a mlogloss of 0.844 using just tfidf vectorizer. Much better than the meta features. We can now create frequency features and see how it is performing.\n",
    "\n",
    "**Naive Bayes on Word Count Vectorizer:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "92ab9f7c-8c2c-4233-a6dc-eaa12ca7d144",
    "_uuid": "9cd43c20764c81a3216b8371c477ae5552e24c7d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Fit transform the tfidf vectorizer ###\n",
    "tfidf_vec = CountVectorizer(stop_words='english', ngram_range=(1,3))\n",
    "tfidf_vec.fit(train_df['text'].values.tolist() + test_df['text'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['text'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['text'].values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "48d68a84-6c6d-40b3-9069-d4c324355fc6",
    "_uuid": "2e05fb42bcd850e74c767961f58400b0dac463ec"
   },
   "source": [
    "Now let us build Multinomial NB model using count vectorizer based features.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "b7927348-b6b1-4960-b76c-ebf14fae37f0",
    "_uuid": "4f9354473820d59076bf991b5110fc4dd309c204"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cv score :  0.450918416166\n"
     ]
    }
   ],
   "source": [
    "cv_scores = []\n",
    "pred_full_test = 0\n",
    "pred_train = np.zeros([train_df.shape[0], 3])\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2017)\n",
    "for dev_index, val_index in kf.split(train_X):\n",
    "    dev_X, val_X = train_tfidf[dev_index], train_tfidf[val_index]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    pred_val_y, pred_test_y, model = runMNB(dev_X, dev_y, val_X, val_y, test_tfidf)\n",
    "    pred_full_test = pred_full_test + pred_test_y\n",
    "    pred_train[val_index,:] = pred_val_y\n",
    "    cv_scores.append(metrics.log_loss(val_y, pred_val_y))\n",
    "print(\"Mean cv score : \", np.mean(cv_scores))\n",
    "pred_full_test = pred_full_test / 5.\n",
    "\n",
    "# add the predictions as new features #\n",
    "train_df[\"nb_cvec_eap\"] = pred_train[:,0]\n",
    "train_df[\"nb_cvec_hpl\"] = pred_train[:,1]\n",
    "train_df[\"nb_cvec_mws\"] = pred_train[:,2]\n",
    "test_df[\"nb_cvec_eap\"] = pred_full_test[:,0]\n",
    "test_df[\"nb_cvec_hpl\"] = pred_full_test[:,1]\n",
    "test_df[\"nb_cvec_mws\"] = pred_full_test[:,2]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5a5d31ea-278c-432a-9840-ca9209410ff7",
    "_uuid": "fb099e48fde5f3960bc2409d323749e855b2e269"
   },
   "source": [
    "Wow. We got a cross validation mlogloss of 0.451 using count vectorizer inplace of tfidf vectorizer. LB score using this model is 0.468. \n",
    "\n",
    "** Naive Bayes on Character Count Vectorizer:**\n",
    "\n",
    "One idea from the \"data eyeballing\" is that counting the special charaters might help. Instead of just counting the special characters, we can use the count vectorizer at character level to get some features. Again we can run Multinomial NB on top of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "aed10880-e717-4a04-85b9-a77f8f58aee9",
    "_uuid": "d26ba4563285c10418479465e10de172f527f2a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cv score :  3.75076392268\n"
     ]
    }
   ],
   "source": [
    "### Fit transform the tfidf vectorizer ###\n",
    "tfidf_vec = CountVectorizer(ngram_range=(1,7), analyzer='char')\n",
    "tfidf_vec.fit(train_df['text'].values.tolist() + test_df['text'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['text'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['text'].values.tolist())\n",
    "\n",
    "cv_scores = []\n",
    "pred_full_test = 0\n",
    "pred_train = np.zeros([train_df.shape[0], 3])\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2017)\n",
    "for dev_index, val_index in kf.split(train_X):\n",
    "    dev_X, val_X = train_tfidf[dev_index], train_tfidf[val_index]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    pred_val_y, pred_test_y, model = runMNB(dev_X, dev_y, val_X, val_y, test_tfidf)\n",
    "    pred_full_test = pred_full_test + pred_test_y\n",
    "    pred_train[val_index,:] = pred_val_y\n",
    "    cv_scores.append(metrics.log_loss(val_y, pred_val_y))\n",
    "print(\"Mean cv score : \", np.mean(cv_scores))\n",
    "pred_full_test = pred_full_test / 5.\n",
    "\n",
    "# add the predictions as new features #\n",
    "train_df[\"nb_cvec_char_eap\"] = pred_train[:,0]\n",
    "train_df[\"nb_cvec_char_hpl\"] = pred_train[:,1]\n",
    "train_df[\"nb_cvec_char_mws\"] = pred_train[:,2]\n",
    "test_df[\"nb_cvec_char_eap\"] = pred_full_test[:,0]\n",
    "test_df[\"nb_cvec_char_hpl\"] = pred_full_test[:,1]\n",
    "test_df[\"nb_cvec_char_mws\"] = pred_full_test[:,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "55e95b9a-807b-4e99-bf95-069a11e07a64",
    "_uuid": "227e565a4a8a6aa04dcfb648688bf0deb0853910"
   },
   "source": [
    "The cross val score is very high and is 3.75. But this might add some different information than word level features and so let us use this for the final model as well.\n",
    "\n",
    "**Naive Bayes on Character Tfidf Vectorizer:**\n",
    "\n",
    "Let us also get the naive bayes predictions on the character tfidf vectorizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cv score :  0.790415258947\n"
     ]
    }
   ],
   "source": [
    "### Fit transform the tfidf vectorizer ###\n",
    "tfidf_vec = TfidfVectorizer(ngram_range=(1,5), analyzer='char')\n",
    "tfidf_vec.fit(train_df['text'].values.tolist() + test_df['text'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['text'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['text'].values.tolist())\n",
    "\n",
    "cv_scores = []\n",
    "pred_full_test = 0\n",
    "pred_train = np.zeros([train_df.shape[0], 3])\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2017)\n",
    "for dev_index, val_index in kf.split(train_X):\n",
    "    dev_X, val_X = train_tfidf[dev_index], train_tfidf[val_index]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    pred_val_y, pred_test_y, model = runMNB(dev_X, dev_y, val_X, val_y, test_tfidf)\n",
    "    pred_full_test = pred_full_test + pred_test_y\n",
    "    pred_train[val_index,:] = pred_val_y\n",
    "    cv_scores.append(metrics.log_loss(val_y, pred_val_y))\n",
    "print(\"Mean cv score : \", np.mean(cv_scores))\n",
    "pred_full_test = pred_full_test / 5.\n",
    "\n",
    "# add the predictions as new features #\n",
    "train_df[\"nb_tfidf_char_eap\"] = pred_train[:,0]\n",
    "train_df[\"nb_tfidf_char_hpl\"] = pred_train[:,1]\n",
    "train_df[\"nb_tfidf_char_mws\"] = pred_train[:,2]\n",
    "test_df[\"nb_tfidf_char_eap\"] = pred_full_test[:,0]\n",
    "test_df[\"nb_tfidf_char_hpl\"] = pred_full_test[:,1]\n",
    "test_df[\"nb_tfidf_char_mws\"] = pred_full_test[:,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**XGBoost model:**\n",
    "\n",
    "Now with the three new variables, we can re-run the xgboost model and evaluate the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "7091aede-e589-45d9-9a85-1560af442725",
    "_uuid": "633eb97dc8b15a7959b962c20222697590071af7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-mlogloss:1.0031\ttest-mlogloss:1.00255\n",
      "Multiple eval metrics have been passed: 'test-mlogloss' will be used for early stopping.\n",
      "\n",
      "Will train until test-mlogloss hasn't improved in 50 rounds.\n",
      "[20]\ttrain-mlogloss:0.412174\ttest-mlogloss:0.416675\n",
      "[40]\ttrain-mlogloss:0.345626\ttest-mlogloss:0.356469\n",
      "[60]\ttrain-mlogloss:0.327476\ttest-mlogloss:0.34384\n",
      "[80]\ttrain-mlogloss:0.315573\ttest-mlogloss:0.33712\n",
      "[100]\ttrain-mlogloss:0.307058\ttest-mlogloss:0.333828\n",
      "[120]\ttrain-mlogloss:0.299592\ttest-mlogloss:0.332\n",
      "[140]\ttrain-mlogloss:0.292843\ttest-mlogloss:0.330182\n",
      "[160]\ttrain-mlogloss:0.286855\ttest-mlogloss:0.32876\n",
      "[180]\ttrain-mlogloss:0.281328\ttest-mlogloss:0.327796\n",
      "[200]\ttrain-mlogloss:0.276372\ttest-mlogloss:0.327127\n",
      "[220]\ttrain-mlogloss:0.271745\ttest-mlogloss:0.32658\n",
      "[240]\ttrain-mlogloss:0.267047\ttest-mlogloss:0.325933\n",
      "[260]\ttrain-mlogloss:0.262644\ttest-mlogloss:0.326123\n",
      "[280]\ttrain-mlogloss:0.257968\ttest-mlogloss:0.325658\n",
      "[300]\ttrain-mlogloss:0.253965\ttest-mlogloss:0.326224\n",
      "[320]\ttrain-mlogloss:0.25024\ttest-mlogloss:0.326444\n",
      "Stopping. Best iteration:\n",
      "[286]\ttrain-mlogloss:0.256745\ttest-mlogloss:0.325469\n",
      "\n",
      "cv scores :  [0.32546941510904698]\n"
     ]
    }
   ],
   "source": [
    "cols_to_drop = ['id', 'text']\n",
    "train_X = train_df.drop(cols_to_drop+['author'], axis=1)\n",
    "test_X = test_df.drop(cols_to_drop, axis=1)\n",
    "\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2017)\n",
    "cv_scores = []\n",
    "pred_full_test = 0\n",
    "pred_train = np.zeros([train_df.shape[0], 3])\n",
    "for dev_index, val_index in kf.split(train_X):\n",
    "    dev_X, val_X = train_X.loc[dev_index], train_X.loc[val_index]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    pred_val_y, pred_test_y, model = runXGB(dev_X, dev_y, val_X, val_y, test_X, seed_val=0, colsample=0.7)\n",
    "    pred_full_test = pred_full_test + pred_test_y\n",
    "    pred_train[val_index,:] = pred_val_y\n",
    "    cv_scores.append(metrics.log_loss(val_y, pred_val_y))\n",
    "    break\n",
    "print(\"cv scores : \", cv_scores)\n",
    "\n",
    "out_df = pd.DataFrame(pred_full_test)\n",
    "out_df.columns = ['EAP', 'HPL', 'MWS']\n",
    "out_df.insert(0, 'id', test_id)\n",
    "out_df.to_csv(\"sub_fe.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7d93a2d6-435a-46bb-a522-c4d6b03d84f0",
    "_uuid": "4512493a6527cc244b9c47eb1ed9ff0dbce5eb5a"
   },
   "source": [
    "**This has a val score of 0.3254 and LB score of 0.3xxx.** Running it on all the folds might give a better score. Now let us check the important variables again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "ed36de2b-e0bb-42e6-bed8-bbbdeb513ba5",
    "_uuid": "ddefa6111b588449df9f2abbc541c0ca051174ab"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tKdkdODYzT4yIm4Cjiva3Z2ZDRBwIfB/Yq537zwVeyszhABHxTqo5uhc4q+hz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f209e65d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the important variables ###\n",
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "xgb.plot_importance(model, max_num_features=50, height=0.8, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Next steps in this FE notebook:**\n",
    "* Using SVD features\n",
    "* Using word embedding based features \n",
    "* Other meta features if any"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ideas for further improvements:**\n",
    "* Parameter tuning for tfidf and count vectorizer\n",
    "* Parameter tuning for naive bayes and XGB models\n",
    "* Ensembling / Stacking with other models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "22214ed1-d307-4dbe-9345-ed3a65c325d8",
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   "source": [
    "\n",
    "**More to come. Stay tuned.!**"
   ]
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